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As shown in Fig. 8b, iiHadoop performs better when the number of nodes increases.
We observe that gaussian approximation matches better and better when the number of mobiles increases.
GridTS is better when the number of tasks executed per resource is high.
Experimental results show that the mobile agent paradigm performs much better when the number of nodes is large while the client/server paradigm is advantageous when the number of nodes is small.
The results show that both SVR-rbf and SVR-poly methods perform better when the number of input variables is higher, and there is no compaction in the non-dimensional input variables.
EDF performs better when the number of MS is less, as each MS does not generate packets all the time, and hence there are far fewer MS requesting for bandwidth.
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Others have proposed different continuity corrections that perform better when the numbers of patients in the intervention and control groups are severely imbalanced [ 5].
Behavioral data showed a clear interference effect (LeFevre interference), in that performance in the non-matching condition was significantly better when the probe number was arithmetically neutral compared to when it was arithmetically related to the cue digits either strongly (i.e., through the product) or weakly (i.e., through its neighbor in the multiplication table).
Higher values perform better when the copy number is very high.
Performance in the non-matching conditions was significantly better when the probe number was arithmetically neutral compared to when it was arithmetically related, either strongly (i.e., the product) or weakly (i.e., a multiple of either cue numbers), to the cue digits.
I kind of liked it better when the Lakers drafted around number 30.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com